




'''simple docstring'''



import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


UpperCAmelCase_							:	Union[str, Any]	     =		'''\

'''

UpperCAmelCase_							:	int	     =		'''
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.

For more information, see https://huggingface.co/docs/transformers/perplexity
'''

UpperCAmelCase_							:	str	     =		'''
Args:
    model_id (str): model used for calculating Perplexity
            NOTE: Perplexity can only be calculated for causal language models.
                    This includes models such as gpt2, causal variations of bert,
                    causal versions of t5, and more (the full list can be found
                    in the AutoModelForCausalLM documentation here:
                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )

    input_texts (list of str): input text, each separate text snippet
        is one list entry.
    batch_size (int): the batch size to run texts through the model. Defaults to 16.
    add_start_token (bool): whether to add the start token to the texts,
        so the perplexity can include the probability of the first word. Defaults to True.
    device (str): device to run on, defaults to \'cuda\' when available
Returns:
    perplexity: dictionary containing the perplexity scores for the texts
        in the input list, as well as the mean perplexity. If one of the input texts is
        longer than the max input length of the model, then it is truncated to the
        max length for the perplexity computation.
Examples:
    Example 1:
        >>> perplexity = datasets.load_metric("perplexity")
        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]
        >>> results = perplexity.compute(model_id=\'gpt2\',
        ...                              add_start_token=False,
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        [\'perplexities\', \'mean_perplexity\']
        >>> print(round(results["mean_perplexity"], 2))
        78.22
        >>> print(round(results["perplexities"][0], 2))
        11.11

    Example 2:
        >>> perplexity = datasets.load_metric("perplexity")
        >>> input_texts = datasets.load_dataset("wikitext",
        ...                                     "wikitext-2-raw-v1",
        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS
        [...]
        >>> input_texts = [s for s in input_texts if s!=\'\']
        >>> results = perplexity.compute(model_id=\'gpt2\',
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        [\'perplexities\', \'mean_perplexity\']
        >>> print(round(results["mean_perplexity"], 2))
        60.35
        >>> print(round(results["perplexities"][0], 2))
        81.12
'''





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					, _KWARGS_DESCRIPTION)
class      lowerCAmelCase (			datasets.Metric):


       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            return datasets.MetricInfo(
                description=_DESCRIPTION     ,	citation=_CITATION     ,	inputs_description=_KWARGS_DESCRIPTION     ,	features=datasets.Features(
                    {
                        '''input_texts''': datasets.Value('''string'''	),
                    }	)     ,	reference_urls=['''https://huggingface.co/docs/transformers/perplexity''']     ,	)




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = 16     ,	__SCREAMING_SNAKE_CASE = True     ,	__SCREAMING_SNAKE_CASE=None	)   ->	Any:



            '''simple docstring'''




            if device is not None:
                 assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
                 if device == "gpu":
                      __snake_case            =	'''cuda'''
            else:
                 __snake_case            =	'''cuda''' if torch.cuda.is_available() else '''cpu'''

            __snake_case            =	AutoModelForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	model.to(__SCREAMING_SNAKE_CASE	)

            __snake_case            =	AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE	)

            # if batch_size > 1 (which generally leads to padding being required), and
            # if there is not an already assigned pad_token, assign an existing
            # special token to also be the padding token
            if tokenizer.pad_token is None and batch_size > 1:
                 __snake_case            =	list(tokenizer.special_tokens_map_extended.values()	)
                 # check that the model already has at least one special token defined
                 assert (
                     len(__SCREAMING_SNAKE_CASE	) > 0
                 ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
                 # assign one of the special tokens to also be the pad token
                 tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]}	)

            if add_start_token:
                 # leave room for <BOS> token to be added:
                 assert (
                     tokenizer.bos_token is not None
                 ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
                 __snake_case            =	model.config.max_length - 1
            else:
                 __snake_case            =	model.config.max_length

            __snake_case            =	tokenizer(
                __SCREAMING_SNAKE_CASE     ,	add_special_tokens=__SCREAMING_SNAKE_CASE     ,	padding=__SCREAMING_SNAKE_CASE     ,	truncation=__SCREAMING_SNAKE_CASE     ,	max_length=__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''     ,	return_attention_mask=__SCREAMING_SNAKE_CASE     ,	).to(__SCREAMING_SNAKE_CASE	)

            __snake_case            =	encodings['''input_ids''']
            __snake_case            =	encodings['''attention_mask''']

            # check that each input is long enough:
            if add_start_token:
                 assert torch.all(torch.ge(attn_masks.sum(1	)     ,	1	)	), "Each input text must be at least one token long."
            else:
                 assert torch.all(
                     torch.ge(attn_masks.sum(1	)     ,	2	)	), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

            __snake_case            =	[]
            __snake_case            =	CrossEntropyLoss(reduction='''none'''	)

            for start_index in logging.tqdm(range(0     ,	len(__SCREAMING_SNAKE_CASE	)     ,	__SCREAMING_SNAKE_CASE	)	):
                 __snake_case            =	min(start_index + batch_size     ,	len(__SCREAMING_SNAKE_CASE	)	)
                 __snake_case            =	encoded_texts[start_index:end_index]
                 __snake_case            =	attn_masks[start_index:end_index]

                 if add_start_token:
                      __snake_case            =	torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0	)	).to(__SCREAMING_SNAKE_CASE	)
                      __snake_case            =	torch.cat([bos_tokens_tensor, encoded_batch]     ,	dim=1	)
                      __snake_case            =	torch.cat(
                          [torch.ones(bos_tokens_tensor.size()     ,	dtype=torch.intaa	).to(__SCREAMING_SNAKE_CASE	), attn_mask]     ,	dim=1	)

                 __snake_case            =	encoded_batch

                 with torch.no_grad():
                      __snake_case            =	model(__SCREAMING_SNAKE_CASE     ,	attention_mask=__SCREAMING_SNAKE_CASE	).logits

                 __snake_case            =	out_logits[..., :-1, :].contiguous()
                 __snake_case            =	labels[..., 1:].contiguous()
                 __snake_case            =	attn_mask[..., 1:].contiguous()

                 __snake_case            =	torch.expa(
                     (loss_fct(shift_logits.transpose(1     ,	2	)     ,	__SCREAMING_SNAKE_CASE	) * shift_attention_mask_batch).sum(1	)
                     / shift_attention_mask_batch.sum(1	)	)

                 ppls += perplexity_batch.tolist()

            return {"perplexities": ppls, "mean_perplexity": np.mean(__SCREAMING_SNAKE_CASE	)}








import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
							from .features import FeatureType


lowerCamelCase_ ,							lowerCamelCase_ ,							lowerCamelCase_    :			Dict    =	False, False, False

@dataclass
class 							_UpperCamelCase					:






			'''simple docstring'''

			__UpperCamelCase   :    Optional[int]          =			None
			__UpperCamelCase   :    bool          =			True
			__UpperCamelCase   :    bool          =			True
			__UpperCamelCase   :    Optional[str]          =			None
			# Automatically constructed
			__UpperCamelCase   :    ClassVar[str]          =			"dict"
			__UpperCamelCase   :    ClassVar[Any]          =			pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}	)
			__UpperCamelCase   :    str          =			field(default="""Audio"""  ,							init=_A  ,							repr=_A	)

			def __call__(							self			:							List[Any]   ):
							return self.pa_type

			def    lowerCAmelCase__						(							self			:							Optional[int]    ,						snake_case_			:							Union[str, bytes, dict]   ):
							try:
											import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
							except ImportError as err:
											raise ImportError("""To support encoding audio data, please install 'soundfile'."""   ) from err
							if isinstance(snake_case_    ,						snake_case_   ):
											return {"bytes": None, "path": value}
							elif isinstance(snake_case_    ,						snake_case_   ):
											return {"bytes": value, "path": None}
							elif "array" in value:
											# convert the audio array to wav bytes
											UpperCamelCase_:				List[Any]						=    BytesIO()
											sf.write(snake_case_    ,						value["""array"""]    ,						value["""sampling_rate"""]    ,						format="""wav"""   )
											return {"bytes": buffer.getvalue(), "path": None}
							elif value.get("""path"""   ) is not None and os.path.isfile(value["""path"""]   ):
											# we set "bytes": None to not duplicate the data if they're already available locally
											if value["path"].endswith("""pcm"""   ):
															# "PCM" only has raw audio bytes
															if value.get("""sampling_rate"""   ) is None:
																			# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
																			raise KeyError("""To use PCM files, please specify a 'sampling_rate' in Audio object"""   )
															if value.get("""bytes"""   ):
																			# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
																			UpperCamelCase_:				str						=    np.frombuffer(value["""bytes"""]    ,						dtype=np.intaa   ).astype(np.floataa   ) / 3_2767
															else:
																			UpperCamelCase_:				Dict						=    np.memmap(value["""path"""]    ,						dtype="""h"""    ,						mode="""r"""   ).astype(np.floataa   ) / 3_2767

															UpperCamelCase_:				Dict						=    BytesIO(bytes()   )
															sf.write(snake_case_    ,						snake_case_    ,						value["""sampling_rate"""]    ,						format="""wav"""   )
															return {"bytes": buffer.getvalue(), "path": None}
											else:
															return {"bytes": None, "path": value.get("""path"""   )}
							elif value.get("""bytes"""   ) is not None or value.get("""path"""   ) is not None:
											# store the audio bytes, and path is used to infer the audio format using the file extension
											return {"bytes": value.get("""bytes"""   ), "path": value.get("""path"""   )}
							else:
											raise ValueError(
											    f'''An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'''   )

			def    lowerCAmelCase__						(							self			:							List[str]    ,						snake_case_			:							dict    ,						snake_case_			:							Optional[Dict[str, Union[str, bool, None]]] = None   ):
							if not self.decode:
											raise RuntimeError("""Decoding is disabled for this feature. Please use Audio(decode=True) instead."""   )

							UpperCamelCase_, UpperCamelCase_:				Any						=    (value["""path"""], BytesIO(value["""bytes"""]   )) if value["""bytes"""] is not None else (value["""path"""], None)
							if path is None and file is None:
											raise ValueError(f'''An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.'''   )

							try:
											import librosa
											import soundfile as sf
							except ImportError as err:
											raise ImportError("""To support decoding audio files, please install 'librosa' and 'soundfile'."""   ) from err

							UpperCamelCase_:				Optional[Any]						=    xsplitext(snake_case_   )[1][1:].lower() if path is not None else None
							if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
											raise RuntimeError(
											    """Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, """
											    """You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """   )
							elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
											raise RuntimeError(
											    """Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, """
											    """You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """   )

							if file is None:
											UpperCamelCase_:				Dict						=    token_per_repo_id or {}
											UpperCamelCase_:				str						=    path.split("""::"""   )[-1]
											try:
															UpperCamelCase_:				Any						=    string_to_dict(snake_case_    ,						config.HUB_DATASETS_URL   )["""repo_id"""]
															UpperCamelCase_:				List[str]						=    token_per_repo_id[repo_id]
											except (ValueError, KeyError):
															UpperCamelCase_:				Any						=    None

											with xopen(snake_case_    ,						"""rb"""    ,						use_auth_token=snake_case_   ) as f:
															UpperCamelCase_, UpperCamelCase_:				List[Any]						=    sf.read(snake_case_   )

							else:
											UpperCamelCase_, UpperCamelCase_:				Union[str, Any]						=    sf.read(snake_case_   )

							UpperCamelCase_:				List[str]						=    array.T
							if self.mono:
											UpperCamelCase_:				int						=    librosa.to_mono(snake_case_   )
							if self.sampling_rate and self.sampling_rate != sampling_rate:
											UpperCamelCase_:				Union[str, Any]						=    librosa.resample(snake_case_    ,						orig_sr=snake_case_    ,						target_sr=self.sampling_rate   )
											UpperCamelCase_:				Dict						=    self.sampling_rate

							return {"path": path, "array": array, "sampling_rate": sampling_rate}

			def    lowerCAmelCase__						(							self			:							Tuple   ):
							from .features import Value

							if self.decode:
											raise ValueError("""Cannot flatten a decoded Audio feature."""   )
							return {
							    "bytes": Value("""binary"""   ),
							    "path": Value("""string"""   ),
							}

			def    lowerCAmelCase__						(							self			:							Any    ,						snake_case_			:							Union[pa.StringArray, pa.StructArray]   ):
							if pa.types.is_string(storage.type   ):
											UpperCamelCase_:				Optional[Any]						=    pa.array([None] * len(snake_case_   )    ,						type=pa.binary()   )
											UpperCamelCase_:				List[str]						=    pa.StructArray.from_arrays([bytes_array, storage]    ,						["""bytes""", """path"""]    ,						mask=storage.is_null()   )
							elif pa.types.is_binary(storage.type   ):
											UpperCamelCase_:				str						=    pa.array([None] * len(snake_case_   )    ,						type=pa.string()   )
											UpperCamelCase_:				List[Any]						=    pa.StructArray.from_arrays([storage, path_array]    ,						["""bytes""", """path"""]    ,						mask=storage.is_null()   )
							elif pa.types.is_struct(storage.type   ) and storage.type.get_all_field_indices("""array"""   ):
											UpperCamelCase_:				Union[str, Any]						=    pa.array([Audio().encode_example(snake_case_   ) if x is not None else None for x in storage.to_pylist()]   )
							elif pa.types.is_struct(storage.type   ):
											if storage.type.get_field_index("""bytes"""   ) >= 0:
															UpperCamelCase_:				Optional[Any]						=    storage.field("""bytes"""   )
											else:
															UpperCamelCase_:				str						=    pa.array([None] * len(snake_case_   )    ,						type=pa.binary()   )
											if storage.type.get_field_index("""path"""   ) >= 0:
															UpperCamelCase_:				List[str]						=    storage.field("""path"""   )
											else:
															UpperCamelCase_:				int						=    pa.array([None] * len(snake_case_   )    ,						type=pa.string()   )
											UpperCamelCase_:				Dict						=    pa.StructArray.from_arrays([bytes_array, path_array]    ,						["""bytes""", """path"""]    ,						mask=storage.is_null()   )
							return array_cast(snake_case_    ,						self.pa_type   )




			def    lowerCAmelCase__						(							self			:							Optional[Any]    ,						snake_case_			:							pa.StructArray   ):

							@no_op_if_value_is_null
							def path_to_bytes(snake_case_			:							Optional[int]   ):
											with xopen(snake_case_    ,						"""rb"""   ) as f:
															UpperCamelCase_:				Optional[int]						=    f.read()
											return bytes_

							UpperCamelCase_:				int						=    pa.array(
							    [
							        (path_to_bytes(x["""path"""]   ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
							        for x in storage.to_pylist()
							    ]    ,						type=pa.binary()    ,						)
							UpperCamelCase_:				Union[str, Any]						=    pa.array(
							    [os.path.basename(snake_case_   ) if path is not None else None for path in storage.field("""path"""   ).to_pylist()]    ,						type=pa.string()    ,						)
							UpperCamelCase_:				Optional[int]						=    pa.StructArray.from_arrays([bytes_array, path_array]    ,						["""bytes""", """path"""]    ,						mask=bytes_array.is_null()   )
							return array_cast(snake_case_    ,						self.pa_type   )






